What a wonderful session and to practice these codes side by side made my concepts even clearer especially enjoyed the pheatmap part ! Please keep up the amazing work Mr LB
Hey thanks very much for this learning, this video is very important to me and very valuable because there are no other videos on CZcams like this. The reason I am saying is that I searched and searched for a week and could not find any videos that explains the practicality and usage of R. Everyone talks about theory as to how to get mean etc how to get median but nobody talks how to interpret these results what does SD of 1.5 mean to you that’s something people don’t talk about thanks very much again for this video and look forward to many more practical videos that will tell you how to get data from the web for example stock market and/or how to do stock analysis on your own using R this would be a very interesting topic if you can I would really appreciate it
Hello, could you please help me, I used this code to fit the trendline of my data, but the line did not show "> abline(lm(Rawdata$Elevation~Rawdata$Bulk, data=Rawdata))". What could be the possible problem? Your video has been helpful as I am a new user of R. I have successfully implemented your tips until this point. Thanks
Hi LB, I am curious to hear your professional opinion about how to fit a model that I find to be challenging. I have measurements of oxygen consumption taken at 4 different temperatures for 520 eggs belonging to 80 species of insects. The goal of my model is to look at how a series of predictors like location, mortality ecc. influence the slopes of the lines that describe the change in oxygen consumption across the 4 measurement temperatures. Oxygen consumption intuitively increases with egg mass, measurement temperature and age of the egg. What I do not understand is how to make these slopes the dependent variables. If I first make a model like oxygen~egg mass+age+measurement temperature and then I use the slopes for each species as dependent variable in a model like slopes~site+mortality+ambient temperature is that correct? Or is that discourage because I am doing statistics on statistics? Conversely, if I build only one model like oxygen~egg mass+age+measurement temperature+site+mortality+ambient temperature is this model actually looking at slopes? Sorry for the long question but I find it hard to get a reasonable answer by myself. Cheers
Where did the inputted data come from (iris)? How should I save my physical lab notebook data? Where did you type in the data to use in R? An excel sheet? A text file? Do you have all your original data from an excel sheet, type it into R all over again without a typo, and then save that data to use later?
Thank you for this informative video so much. Your videos are always helpful to me. I have a question about PCA. If my PC1 is around 39%, PC2 is 23%, and PC1~PC4 cover over 80% of RNA seq data, should I use PC1 and PC2 for the PCA plot?
It really depends on your tolerance to the coverage, you can try to run a scree plot to check the coverage and decide based on the elbow of the scree plot. You can also check this video on how to create a plot in 3D to allow you to check 3 dimension at the same time . For non linear relationship in the data, you might also want to try MDS, TSNE and UMAP~~~ 3d plot(czcams.com/video/TlXSlV4Ng40/video.html)
Thanks dear for this informative video. I'm basic learner who want to do pca, correlation, and regression model with my ecological data set R. Can you help me?
I don't think there's a standard way to deal with skewness ot outliers without understand the nature of the data. Some required moving the outlier, some required transformation/normalization. One of the example I have talked about is the use of negative binomial model.in the. Gene expression in the Deseq2 program (you can check it out here czcams.com/video/lJw6Ku_jQkM/video.html)
Around the 10:40 mark, you talk about the difference between r2 and p-values, but I think you make some minor mistake. r2 value tells you what percentage of teh variation in y is explained by changes in x - i.e. how much of the variation is explained by the line. The p-value tells you whether the gradient of the line is significantly different from zero.
Single best video which teaches r and statistics the best way possible
One of the best R-tutorials so far
Mr LB, your efforts in explaining aspects of R, and in presenting the material to that end, are heaven sent! Thank you so very much!
Mr LB, l would like to say thank you, l love how you just made R so easy for me to navigate around... Deeply appreciate the presentation you did
What a wonderful session and to practice these codes side by side made my concepts even clearer especially enjoyed the pheatmap part ! Please keep up the amazing work Mr LB
This is very concise and explicit. Thanks for sharing
Thank you a lot for this excellent presentation and teaching.
Thank you so much, for this video, which reconciles me with R🤩
Absolutely brilliant, clear, concise, and articulate...Thank you so much
Excellent presentation and thank you so much.
Hey thanks very much for this learning, this video is very important to me and very valuable because there are no other videos on CZcams like this. The reason I am saying is that I searched and searched for a week and could not find any videos that explains the practicality and usage of R.
Everyone talks about theory as to how to get mean etc how to get median but nobody talks how to interpret these results what does SD of 1.5 mean to you that’s something people don’t talk about thanks very much again for this video and look forward to many more practical videos that will tell you how to get data from the web
for example stock market and/or how to do stock analysis on your own using R this would be a very interesting topic if you can I would really appreciate it
You're the best, you made it simple and sweet.
Many thanks sir! Thank you for creating the informative and engaging video👍
You made this very simple. Thank you so much.
These videos are brilliant, thank you!
really nice tutorial man! big shout out from brazil
Nice work
8:54 there's a mistake in that Formula, it should be lm(y~x) not lm(x~y)
"Student" was the pseudonym of William Gosset, who invented the t-test.
THANKS!!!
Hello, could you please help me, I used this code to fit the trendline of my data, but the line did not show "> abline(lm(Rawdata$Elevation~Rawdata$Bulk, data=Rawdata))". What could be the possible problem? Your video has been helpful as I am a new user of R. I have successfully implemented your tips until this point. Thanks
Hi LB, I am curious to hear your professional opinion about how to fit a model that I find to be challenging. I have measurements of oxygen consumption taken at 4 different temperatures for 520 eggs belonging to 80 species of insects. The goal of my model is to look at how a series of predictors like location, mortality ecc. influence the slopes of the lines that describe the change in oxygen consumption across the 4 measurement temperatures. Oxygen consumption intuitively increases with egg mass, measurement temperature and age of the egg. What I do not understand is how to make these slopes the dependent variables. If I first make a model like oxygen~egg mass+age+measurement temperature and then I use the slopes for each species as dependent variable in a model like slopes~site+mortality+ambient temperature is that correct? Or is that discourage because I am doing statistics on statistics? Conversely, if I build only one model like oxygen~egg mass+age+measurement temperature+site+mortality+ambient temperature is this model actually looking at slopes? Sorry for the long question but I find it hard to get a reasonable answer by myself.
Cheers
Good stuff
hello，could you please tell me how to do autoencoder anaysis for raster in rstudio?
how do I add my own data into R ?? I'm very new in this field pls help me :)
Where did the inputted data come from (iris)? How should I save my physical lab notebook data? Where did you type in the data to use in R? An excel sheet? A text file? Do you have all your original data from an excel sheet, type it into R all over again without a typo, and then save that data to use later?
Iris is actually build into the Rstudio envionment, you can just type in and it should work
Thank you for this informative video so much. Your videos are always helpful to me. I have a question about PCA. If my PC1 is around 39%, PC2 is 23%, and PC1~PC4 cover over 80% of RNA seq data, should I use PC1 and PC2 for the PCA plot?
It really depends on your tolerance to the coverage, you can try to run a scree plot to check the coverage and decide based on the elbow of the scree plot. You can also check this video on how to create a plot in 3D to allow you to check 3 dimension at the same time .
For non linear relationship in the data, you might also want to try MDS, TSNE and UMAP~~~
3d plot(czcams.com/video/TlXSlV4Ng40/video.html)
Thanks dear for this informative video. I'm basic learner who want to do pca, correlation, and regression model with my ecological data set R. Can you help me?
Feel free to sent me a mail at liquidbrain.r@gmail.com
Would you please share a link about how to deal with non parametric or skewed data and outliers? Thanks
I don't think there's a standard way to deal with skewness ot outliers without understand the nature of the data. Some required moving the outlier, some required transformation/normalization. One of the example I have talked about is the use of negative binomial model.in the. Gene expression in the Deseq2 program (you can check it out here czcams.com/video/lJw6Ku_jQkM/video.html)
Around the 10:40 mark, you talk about the difference between r2 and p-values, but I think you make some minor mistake.
r2 value tells you what percentage of teh variation in y is explained by changes in x - i.e. how much of the variation is explained by the line. The p-value tells you whether the gradient of the line is significantly different from zero.
This said, your explanations of R are awesome - thank you!
It is a very good lecture, but, the demonstration is too fast to understand for biggners.